Carbohydrate molecular weight profiling, sequence, linkage, and branching data: ES-MS and CID

This section summarizes several strategies for a more complete understanding of carbohydrate structure with a focus on glycolipids and glycoprotein glycans. The techniques include periodate oxidation to impart greater molecular specificity to isomeric glycans, methylation to improve sensitivity and the information content within CID spectra, electrospray for "soft" and efficient ionization, and CID to obtain structural detail. The lipophilicity of the products following derivatization contributes to product cleanup by solvent extraction and enhances sensitivity during ES. When combined with CID information, this yields sequence, linkage, and branching information. Oxidation and reduction preceding methylation augments CID analysis with an altered structure that can be profiled at the same sensitivity. Within the context of established motifs, these contrasting profiles corroborate glycan structure and specifically identify isobaric elements transparent in the initial profile. An earlier report indicating ring-opening fragments were essentially absent in low-energy collisions of methylated and natriated oligosaccharides contrasts our observations. However, as this report used a methylated oligomer containing an internal N-acetylhexose as an illustration, the conclusion is plausible (cf., Figure 9). The poor ionization efficiency of FAB and the high matrix background limit the dynamic range in the CID spectrum and, thereby, the ability to unambiguously identify weaker peaks. It would be expected that high-energy CID affords a broader range of fragment types, including ring-opening fragments. In terms of a structural methodology, this is ambivalent since the increase in fragmentation pathways also applies to small molecule eliminations which are usually less informative. In ES-CID-MS, the carbohydrate chemist has a powerful new tool in hand for structural elucidations that can be conducted at the low-picomole level. Parallel developments can be expected to continue for other ionization methods, in particular matrix-assisted desorption/ionization on linear and reflectron time of flight mass spectrometers, and improvement in the performance and sensitivity of high-resolution mass analyzers through the use of focal plane detectors and more sophisticated hardware and software for Fourier transform ion cyclotron resonance mass measurements. These have, as yet, only begun to be applied to carbohydrate structural analysis but should add still more versatility to experimental design in the future.     

Generating Time-Series Data Using Generative Adversarial Networks for Mobility Demand Prediction

The increasing penetration rate of electric kickboard vehicles has been popularized and promoted primarily because of its clean and efficient features. Electric kickboards are gradually growing in popularity in tourist and education-centric localities. In the upcoming arrival of electric kickboard vehicles, deploying a customer rental service is essential. Due to its free-floating nature, the shared electric kickboard is a common and practical means of transportation. Relocation plans for shared electric kickboards are required to increase the quality of service, and forecasting demand for their use in a specific region is crucial. Predicting demand accurately with small data is troublesome. Extensive data is necessary for training machine learning algorithms for effective prediction. Data generation is a method for expanding the amount of data that will be further accessible for training. In this work, we proposed a model that takes time-series customers’ electric kickboard demand data as input, pre-processes it, and generates synthetic data according to the original data distribution using generative adversarial networks (GAN). The electric kickboard mobility demand prediction error was reduced when we combined synthetic data with the original data. We proposed Tabular-GAN-Modified-WGAN-GP for generating synthetic data for better prediction results. We modified The Wasserstein GAN-gradient penalty (GP) with the RMSprop optimizer and then employed Spectral Normalization (SN) to improve training stability and faster convergence. Finally, we applied a regression-based blending ensemble technique that can help us to improve performance of demand prediction. We used various evaluation criteria and visual representations to compare our proposed model’s performance. Synthetic data generated by our suggested GAN model is also evaluated. The TGAN-Modified-WGAN-GP model mitigates the overfitting and mode collapse problem, and it also converges faster than previous GAN models for synthetic data creation. The presented model’s performance is compared to existing ensemble and baseline models. The experimental findings imply that combining synthetic and actual data can significantly reduce prediction error rates in the mean absolute percentage error (MAPE) of 4.476 and increase prediction accuracy.     

Selection of industrial robots using compromise ranking and outranking methods

Selection of a robot for a specific industrial application is one of the most challenging problems in real time manufacturing environment. It has become more and more complicated due to increase in complexity, advanced features and facilities that are continuously being incorporated into the robots by different manufacturers. At present, different types of industrial robots with diverse capabilities, features, facilities and specifications are available in the market. Manufacturing environment, product design, production system and cost involved are some of the most influencing factors that directly affect the robot selection decision. The decision maker needs to identify and select the best suited robot in order to achieve the desired output with minimum cost and specific application ability. This paper attempts to solve the robot selection problem using two most appropriate multi-criteria decision-making (MCDM) methods and compares their relative performance for a given industrial application. The first MCDM approach is ‘VIsekriterijumsko KOmpromisno Rangiranje’ (VIKOR), a compromise ranking method and the other one is ‘ELimination and Et Choice Translating REality’ (ELECTRE), an outranking method. Two real time examples are cited in order to demonstrate and validate the applicability and potentiality of both these MCDM methods. It is observed that the relative rankings of the alternative robots as obtained using these two MCDM methods match quite well with those as derived by the past researchers.     

A new social and momentum component adaptive PSO algorithm for image segmentation

In this paper, we present a new variant of Particle Swarm Optimization (PSO) for image segmentation using optimal multi-level thresholding. Some objective functions which are very efficient for bi-level thresholding purpose are not suitable for multi-level thresholding due to the exponential growth of computational complexity. The present paper also proposes an iterative scheme that is practically more suitable for obtaining initial values of candidate multilevel thresholds. This self iterative scheme is proposed to find the suitable number of thresholds that should be used to segment an image. This iterative scheme is based on the well known Otsu’s method, which shows a linear growth of computational complexity. The thresholds resulting from the iterative scheme are taken as initial thresholds and the particles are created randomly around these thresholds, for the proposed PSO variant. The proposed PSO algorithm makes a new contribution in adapting ‘social’ and ‘momentum’ components of the velocity equation for particle move updates. The proposed segmentation method is employed for four benchmark images and the performances obtained outperform results obtained with well known methods, like Gaussian-smoothing method (Lim, Y. K., & Lee, S. U. (1990). On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques. Pattern Recognition, 23, 935–952; Tsai, D. M. (1995). A fast thresholding selection procedure for multimodal and unimodal histograms. Pattern Recognition Letters, 16, 653–666), Symmetry-duality method (Yin, P. Y., & Chen, L. H. (1993). New method for multilevel thresholding using the symmetry and duality of the histogram. Journal of Electronics and Imaging, 2, 337–344), GA-based algorithm (Yin, P. -Y. (1999). A fast scheme for optimal thresholding using genetic algorithms. Signal Processing, 72, 85–95) and the basic PSO variant employing linearly decreasing inertia weight factor.     

User-input driven QoS management in ad hoc networks

Though the network quality-of-service (QoS) metrics are defined in terms of technical parameters (e.g., delay, jitter, bandwidth), they are rather subjective when it comes to the end user. Oftentimes, the end user finds it difficult to express his desired QoS in such technical parameters, though he has a fair idea of what QoS he desires. In this paper, we show how translation functions can be devised and used to translate the user inputs to networking parameters that are used by various layers of the protocol stack. In particular, we consider an ad hoc network and show an interface design that uses translation functions to map user supplied inputs to parameters at the medium access control (MAC) and routing layers. These parameters, in turn, choose the right strategies that particular layer functionality can adopt, such that the QoS desired by the user is achieved. We also compute the associated costs due to the different strategies adopted. We implement the interface on ns-2 and conduct simulation experiments with randomly scattered IEEE 802.11 enabled nodes. Results show the functionality of the interface and demonstrate how delay, throughput, and network lifetime are affected when the end user seeks different levels of QoS.     

Experimental characterization of piezoelectrically actuated micromachined silicon valveless micropump

In this paper, performance of piezoelectrically actuated pyramidal valveless micropumps is studied experimentally in detail. Valveless micropumps based on silicon and glass substrate are fabricated using MEMS technology. Two different sizes of micropumps having overall dimensions of 5 mm × 5 mm × 1 mm and 10 mm × 10 mm × 1 mm are fabricated and characterized. In the fabricated micropumps, the thickness of silicon diaphragm is <20 µm which gives the advantage of operating pump at low voltage with excellent stability and consistency. The performance of micropumps in terms of flowrate and backpressure is evaluated for a wide range of driving frequency and actuating voltages. The maximum flowrate of water in the 10-mm micropump is 355 µl/min and backpressure of 3.1 kPa at zero flowrate for an applied voltage of 80 V at frequency 1.05 kHz. The reported micropumps have low footprint, high flowrate and backpressure. Thus, these micropumps are especially suited for biological applications as these can withstand adequate amount of backpressure. Comparative study of the performance of these micropumps with those available in the literature brings out the efficacy of these micropumps.     

Complexity of points-to analysis of Java in the presence ofexceptions

At each program point, points-to analysis for statically typed object oriented programming languages (e.g., Java, C++) determines those objects to which a reference may refer (or a pointer may point) during execution. Points-to analysis is necessary for any semantics based software tools for object oriented systems. Our new complexity results for points-to analysis distinguish the difficulty of intraprocedural and interprocedural points-to analyses for languages with combinations of single-level types (i.e., types with data members only of primitive type), exceptions with or without subtyping, and dynamic dispatch. Our results include: 1) the first polynomial-time algorithm for points-to analysis in the presence of exceptions that handles a robust subset of Java without threads and can be applied to C++; 2) proof that the above algorithm is safe, in general, and provably precise on programs with single-level types and exceptions without subtyping, but not dynamic dispatch, thus, this case is in P; 3) proof that an interprocedural points-to analysis problem with single-level types and exceptions with subtyping, but without dynamic dispatch, is PSPACE-hard, while the intraprocedural problem is PSPACE-complete. Other complexity characterizations of points-to analysis in programs without exceptions are presented, including an algorithm with worst-case bound of O(n⁵ ), which improves over the O(n⁷) worst-case bound achievable from previous approaches of T. Reps et al. (1995) and W.A. Landi and B.G. Ryder (1991)     

Monomers of human beta 1 beta 1 alcohol dehydrogenase exhibit activity that differs from the dimer

A previously unreported enzymatic activity is described for monomers of the beta 1 beta 1 isoenzyme of human alcohol dehydrogenase that were prepared from dimeric enzyme by freeze-thaw in liquid nitrogen. Whereas the dimeric enzyme has optimal activity at low substrate concentrations (2.5 mM ethanol, 50 microM NAD+; "low Km" activity), the monomer has its highest activity at high substrate concentrations (1.5 M ethanol, 2.5 mM NAD+; "high Km" activity). While the activity of the monomer does not appear to be saturated at 1.5 M ethanol, its maximal activity at this high ethanol concentration exceeds the Vmax of the dimer by about 3-fold. The apparent Km of NAD+ with monomers is 270 microM, and no activity could be detected with nicotinamide mononucleotide as cofactor. During gel filtration the high Km activity elutes at a lower apparent molecular weight position than the dimer. The kinetics of monomer-to-dimer reassociation are consistent with a second-order process with a rate constant of 240 M-1 s-1. The reassociation rate is markedly enhanced by the presence of NAD+. During refolding of beta 1 beta 1 following denaturation in 6 M guanidine hydrochloride, an enzyme species with high Km activity and spectral properties similar to the freeze-thaw monomer is observed, indicating that a catalytically active monomer is an intermediate in the refolding pathway. The enzymatic activity of the monomer implies that the intersubunit contacts of beta 1 beta 1 are not crucial in establishing a catalytically competent enzyme. However, the differences in specific activity and Km between monomer and dimer suggest that dimerization may serve to modulate the catalytic properties.     

Software fault prediction using Nonlinear Autoregressive with eXogenous Inputs (NARX) network

This paper explores a new approach for predicting software faults by means of NARX neural network. Also, a careful analysis has been carried out to determine the applicability of NARX network in software reliability. The validation of the proposed approach has been performed using two real software failure data sets. Comparison has been made with some existing parametric software reliability models as well as some neural network (Elman net and TDNN) based SRGM. The results computed shows that the proposed approach outperformed the other existing parametric and neural network based software reliability models with a reasonably good predictive accuracy.